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Title: Economics of Climate Adaptation (ECA) –  Shaping climate-resilient development

A factsheet on rural resilience

Authors: Dr. David Bresch, Andreas Spiegel, Lea Müller

Editing and realisation: Patrick Reichenmiller

Graphic design and production: Swiss Re Corporate Real Estate & Logistics/ Media Production, Zürich

Visit www.swissre.com to download or to order  additional copies of Swiss Re publications.

05/13

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Economics of Climate Adaptation (ECA) – Shaping climate-resilient development A framework for decision-making

Making rural communities more resilient to the impact of climate change requires a comprehensive portfolio of adaptation measures. But decision-makers need the facts to identify the most cost-effective investments.

Climate adaptation is an urgent priority for the custodians of national and local economies, such as finance ministers and mayors. Such decision-makers ask: What is the potential climate-related loss to our economies and societies over the coming decades? How much of that loss can we avert, with what measures? What investment will be required to fund those measures – and will the benefits of that investment outweigh the costs?

The ECA methodology¹ provides decision-makers with a fact base to answering these questions in a systematic way. It enables them to understand the impact of climate on their economies – and identify actions to minimize that impact at the lowest cost to society. Hence it allows decision-makers to integrate adaptation with economic development and sustainable growth.

In essence, we provide a methodology to pro-actively manage total climate risk, which means:  Assess today’s climate risk  Chart out the economic development paths that put greater population and assets 

at risk  Consider the additional risks presented by climate change

1  The methodology is based on the findings of a study by the Economics of Climate Adaptation Working Group, a partnership between the Global Environment Facility, McKinsey & Company, Swiss Re, the Rocke feller Foundation, ClimateWorks Foundation,  the European Commission, and Standard Chartered Bank. See reference 5 below.

Background

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2 

Where and from what are we at risk?

How could we respond?

What is the magnitude of the expected loss?

Example Georgetown, Guyana: Guyana faces high risk of flash floods  as large areas lie below sea level and  cannot easily drain after rainstorms.  The expected loss due to flash floods  in the high climate change scenario  more than doubles by 2030. 

For a given region, (economic) sectors and affected population, in the first step, we  indentify the most relevant hazards and analyze historical events (eg from disaster  datasets).

Using state-of-the-art probabilistic modeling, we estimate the expected economic loss today, the incremental increase from economic growth and the further incremental  increase due to climate change.

Among the various factors, future change in climate risk is the most difficult to predict. We therefore use scenario analysis2 as the main tool to help decision makers deal with uncertainty, constructing three potential climate risk scenarios: Today‘s climate, moderate and high climate change, normally for 20303.

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Source: Report of the Economics of Climate Adaptation Working Group 2009

We then build a balanced portfolio of adaptation measures, assessing the loss aversion potential and cost-benefit ratio for each adaptation measure4. The loss aversion  potential (the benefit of the measure) is assessed by modeling the effect of the specific measure, the cost by calculating capital and operating expenditures.

The adaptation cost curve illustrates that a balanced portfolio of prevention, intervention and insurance measures are available to pro-actively manage total climate risk.  Insurance – risk transfer – incentives prevention initiatives by putting a price tag on the risk premium.

2  To arrive at these scenarios, we use global and regional circulation models to assess changes in precipitation and temperature, mainly based on the A2 IPCC 4th AR emission scenario. We leverage public academic  research to flesh out the complex interactions between climate change and potential impact (for example, between increases in sea surface temperature and hurricane intensity).

3  We choose 2030 as this is far enough in the future to result in a climate change impact, close enough  to be relevant for decision-making. Any other timeframe could be assessed with the same methodology.

4  Since the probabilistic loss modeling is carried out at high resolution (postal code or higher) and taking  into account the specific vulnerabilities of all assets involved, the effect of adaptation measures is reflected in a highly detailed fashion, too (eg exact position of flood defenses…)

0

50

100

150

200

250

+109%

+93%

+202%

66

61

72 199

2008, today’s expected loss

2030, total expected loss

Incremental increase from economic growth; no climate change

Incremental increase from climate change

Expected loss from exposure to climateHigh climate change scenario, 2008 USD millions

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   3

Economics of Climate Adaptation case studies:

Source: Report of the Economics of Climate Adaptation Working Group 2009

So far, economics of climate adaptation studies have been carried out for5: Georgetown, Guyana: focus on risk from flash floods; Miami and South Florida, USA: focus on risk from hurricanes; Hull, UK: focus on risk from multiple hazards (wind, inland flood, storm surge…); Mopti region, Mali: focus on risk to agriculture from climate zone shift; Maharashtra, India and North and North East China: focus on drought risk to agriculture; Samoa: focus on risks caused by sea level rise (storm surge and groundwater salination); Tanzania: focus on health and power risks caused by drought; Caribbean: Multihazard and –sector studies in Anguilla, Bermuda, Barbados , Jamaica, Antigua and Barbuda, St. Lucia and in Dominica; and a sector study along the US Gulf Coast (Alabama, Louisiana, Mississippi, Texas).

In the 17 studies carried out so far5, we learn, that (at least until 2030):  The key drivers in many cases are today’s climate risk and economic development.  The prioritization of the adaptation measures is not strongly dependent on the 

chosen climate change scenario. Cost-effectiveness is still valid even without climate change for a substantial subset of proposed measures 

This presents a strong case for immediate action – it is cheaper to start adaptation now than to sit and wait.

0

0.2

0.4

0.6

0.8

1

0 200 400 600 800

Adaptation Cost Curve

Cost/benefit ratio

Measures below this line have net economic benefit

0.09 0.10 0.16

0.60

0.03 0.30

8.80

Early warning infrastructure

Building codes for new construction

Drainage system maintenance

Drainage system upgrade

Flood resistant rice seeds

Conservancy repair

Mass relocation of agriculture

Averted loss (2008 USD millions)

1.98

No climate change

High climate change

Example Georgetown, Guyana: The adaptation cost curve for flood risk in Georgetown, Guyana (see referenced report5 for details). For each adaptation measure (rectangle), the loss aversion po-tential (horizontal axis) and its cost/benefit ratio (vertical axis) is shown. For the high climate change scenario more than 60% of the expected loss can be cost-effectively averted by prevention and intervention measures. In particular note that the con-servancy repair measure is cost-effective in the high climate change scenario, as opposed to the no climate change scenario where the cost/benefit ratio is 1.98 (light blue bar).

5   Method description and first eight case studies across the globe: http://media.swissre.com/documents/rethinking_shaping_climate_resilent_development_en.pdf

  Latest report assessing adaptation needs in the Caribbean region:http://media.swissre.com/documents/ECA+Brochure-Final.pdf

  Report on building a resilient energy Gulf Coast: http://media.swissre.com/documents/Entergy_study_exec_report_20101014.pdf

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